Identifying Individualized Multiple Chronic Condition Pathways Associated with Dementia

Background: Patients with dementia frequently present with multiple chronic conditions (MCCs). Growing evidence indicates these conditions do not co-occur by chance but may interact in ways that accelerate cognitive decline. In this study, we presented a machine-learning framework to identify these non-random, personalized MCC patterns associated with dementia, based on patients’ pre-existing conditions. Furthermore, we demonstrated a patient example illustrating how this approach enables the early detection of emerging MCC trajectories. Methods: To address this challenge, we introduced a lazy decision tree (lazyDT-MCCPath) framework using National Alzheimer’s Coordinating Center data. For each patient, lazyDT-MCCPath dynamically fine-tunes its training distribution based on pre-existing conditions from the preceding 3-5 years, while remaining MCCs were used to predict dementia risk. The decision tree provides a transparent pathway showing which emerging MCCs were associated with higher dementia risk. Results: Unlike traditional eager decision trees (eagerDT), which lacked sufficient training data for 17%-33% of patient profiles, lazyDT-MCCPath successfully modeled all profiles and achieved a 0.17 improvement in AUROC (0.66 vs. 0.83). Conclusions: lazyDT-MCCPath effectively translates pre-existing MCC profiles into personalized dementia risk probabilities for emerging conditions, which may help clinicians prioritize MCCs to monitor more closely.